Recently, the authors have proposed `data-driven resolvent analysis'
as an efficient method for learning the singular triplets
of a system purely from data measurements [1]}.
The method is analogous to DMD, except instead of computing
the eigenmodes via eigendecomposition, we compute the resolvent modes
via a singular value decomposition.
In the following example, we demonstrate that piDMD can be integrated
into data-driven resolvent analysis to provide physics-informed diagnostic
information about the optimal forcings and responses of the system.
